There are numerous Internet web sites and other systems that involve interactions between “users” and “items”. A user is a person, system, or a component that interacts with a computer software application. An item is a tangible or non-tangible thing. For example, an e-commerce web site allows “users” to browse through, and possibly purchase, various “items”. Many of these web sites and similar systems allow users to evaluate the various items, and these evaluations are generally described as user-item ratings.
Many of these web sites, such as Netflix and Dell, and similar systems, display an average evaluation, or an “average rating” of each item. However, such “average ratings” do not reflect the tastes and judgments of any single user. Therefore, any given user might find their actual rating of an item to be very different than the average user-item rating.
Some web site systems have attempted to measure the tastes and judgments of users, and then calculate and display item ratings specifically for each user. This has typically been accomplished through combinations of two general methods.
The first of these two general methods involves measuring each user's similarity or dissimilarity to other users through the user-item ratings each user has previously submitted on other items, and then recommending items that statistically similar users rated highly, and not recommending items that statistically similar users rated poorly. User communities are formed consisting of groups of users that share the similar ratings on a same set of items. The target user will then be placed into a user community where their ratings on the same set of items are similar to the community. The rating that is then reported to all users in the community is based only upon the members of the community, and not the average of all participants in all communities. The average rating of all communities, which is the statistical average of all data, can also be displayed. This method will be referred to as a user grouping technique where a user may see “Users like you” or “My rating” while accessing web sites. An item's attributes, other than its rating, are irrelevant the calculation in this grouping technique.
The second of these two general methods involves determining a user's like or dislike for various items' attributes (e.g., “color”, “size”, or “price”); and then recommending items that possess attributes that the user already rated highly, and not recommending items that possess attributes that the user already rated poorly. Items that receive high ratings are grouped into one group, and items that receive poor ratings are grouped into another group from only a specific user. Within each group, each item's attributes are found. Alternatively, all attributes of all items are found. Common attributes amongst the items within a group are then selected. Suggestions for other items with similar attributes as the common attributes can then be made to the user. Systems utilizing this method include Dell, CoffeeGeek, and Zagats.
However, the two methods described above have drawbacks. A drawback of the first general method is that systems often have numerous relatively new users that have not submitted many user-item ratings. It is therefore difficult to accurately measure these new users' similarity or dissimilarity to other users. Therefore, it is difficult to accurately recommend items to these new users. It is also difficult to place these new users in a community with other users who have rated items in a similar way.
A drawback of the second general method is that one must know, measure, store, sort, and select information about the varying attributes of the items. Although attributes can be objective and factual, they may also be based on subjective contexts. Another drawback of this general method is the tendency to stereotype user likes and dislikes by the very process of categorizing items by perceived attributes. The weighting, or strength, of a particular items' attributes to the overall rating is also typically unknown.